Medical terminology is voluminous, fragmented, and complex. Multiple standards bodies (e.g., Health Level Seven (HL7), World Health Organization (WHO), etc.) make contributions to categorizing and publishing medical vocabularies (e.g. Systematized Nomenclature of Medicine (SNOMED), International Classification of Diseases (ICD), Logical Observation Identifier Names and Codes (LOINC), etc.) across multiple healthcare domains (e.g., medical procedures, problem lists, laboratory, etc.).
Most approaches to managing terminologies rely on mapping rules and use of human intervention of terminology engineers or medical coders to understand differences across source vocabularies, to rationalize the organization of data (via hierarchies and relationships), to identify differences in granularity, and to map between codes and synonyms where there is overlap. This process requires a large amount of manpower to maintain an updated vocabulary and is especially burdensome when implementing new systems in an established healthcare organization with an abundance of systems and proprietary codes and synonyms. Combined with internationalization and a desire to share data across healthcare organizations, the problem quickly becomes unmanageable. For this reason, many healthcare IT providers have created their own proprietary codes, relationships, terms, and picklists which remain unintegrated with other systems and terminologies. Otherwise, the human effort involved can occupy a team of humans for months to find matches between terminologies.